code: def covariance_matrix(self) -> Tensor:
      Kxx = self.trunc.covariance_matrix
      Kxy = self.cross_covariance_matrix
      Kyy = self.gauss.covariance_matrix
      return block_matrix_concat(
          blocks=([Kxx, Kxy], [Kxy.transpose(-2, -1), Kyy])
      )
  
imports: from __future__ import annotations
  from inspect import getmembers
  from typing import Optional, Sequence, Union
  import torch
  from botorch.utils.probability.linalg import augment_cholesky, block_matrix_concat
  from botorch.utils.probability.mvnxpb import MVNXPB
  from botorch.utils.probability.truncated_multivariate_normal import TruncatedMultivariateNormal
  from linear_operator.operators import LinearOperator
  from linear_operator.utils.errors import NotPSDError
  from torch import Tensor
  from torch.distributions.multivariate_normal import Distribution, MultivariateNormal
  from torch.distributions.utils import lazy_property
  from torch.nn.functional import pad
label_desc: Use image node_id 7 for calling the UnifiedSkewNormal obj's underlying member method code with example usage: obj.covariance_matrix() and returns: block_matrix_concat